Checkpoint K8s pods transparently (plain CPU or GPU accelerated) [video]

Hacker News

Hi HN, I am an early adopter of containers with a background in HPC. From the early days, I’ve tried to merge container tech into the HPC (and HTC) stack. Containers already make packing and deployment easier - especially in AI/ML and data science. How about checkpoint/restore? Over the last couple of months, we at MemVerge have developed a Kubernetes Operator for transparent checkpointing and restoring, allowing you to use discounted Spot instances for long-running workloads, like bioinformatics workflows or ML training. Here’s how it works: - the operator attaches a PVC to your pod - intercepts the STOP signal to checkpoint the pod - if the attached PVC contains a checkpoint when the pod is starting over, it will be restored instead of starting from scratch. Here’s a 2m30s video that demonstrates interrupting a small training workload: https://youtu.be/K9yY6_2255Y This can be triggered by someone draining the node (e.g., due to an EC2 Spot reclaim), deleting a pod, or another operator acting on its own logic. Our checkpoint engine captures every aspect of the process tree within the container: memory pages, file descriptors—even TCP connections, if you want us to. Until recently, it was targeted at CPU use cases only. We’ve now added support for NVIDIA GPUs, with AMD GPUs coming soon (via upstream CRIU plugins). I’ve done some typical checkpoint/restore work (e.g., Jupyter notebooks, traditional jobs) and would love to hear what kinds of workloads you’re interested in checkpointing and restoring. You can try it out in your Kubernetes environment with our 60-day trial: https://form.typeform.com/to/vZujMYxI

Visit product View on Hacker NewsJun 27, 2025

Share card

Actual performance

2points

4comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: plain · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, month · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Metaballs Implementation in the CPU and GPU58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Metaballs Implementation in the CPU and GPU

Hacker News2

Video about the CPU vulnerability Zenbleed (CVE-2023-20593)50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Video about the CPU vulnerability Zenbleed (CVE-2023-20593)

Hacker News4

Bottleneck Calculator44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Find out if your CPU or GPU is holding your PC back

Indie Hackers1games

Harvard CPU in Verilog and Assembler in Go54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Harvard CPU in Verilog and Assembler in Go

Hacker News4

H2 Forth CPU58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

H2 Forth CPU

Hacker News120

PicoVision – A HDMI video stick with dual RP2040 chips (CPU and GPU)53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PicoVision – A HDMI video stick with dual RP2040 chips (CPU and GPU)

Hacker News7

Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU

Hacker News10

CPU Ranker38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

CPU Databse, CPU, Processors, Mobile CPU, CPU List

Product Hunt+4

Tilery-VM – run Nvidia cuTile GPU kernels on a CPU, no GPU required60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Tilery-VM – run Nvidia cuTile GPU kernels on a CPU, no GPU required

Hacker News4

Online CPU profiler for Golang59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Online CPU profiler for Golang

Hacker News1

Browse all products Analyze your launch